Applied ML | IU - Spring 2021

---

Course Topics

Classification

KNN

Crossfold Validation

Optimization

Root Finding

Gradient Descent

Analytical v. Numerical Optimization

Constrained Optimization

Convex Optimization

Regression

Linear Regression

Simple Linear Regression

Multiple Linear Regression

Brute-Force LR via Gradient Descent

LR via Normal Equation

LR via Gradient Descent (Numerical Optimization)

LR via Gradient Descent (graph form)

Extensions to Linear Regression

Gradient Descent

Polynomial Regression

Bias - Variance Tradeoff

Feature Selection

Ridge Regression Regularization

LASSO Regularization

Subset Selection

Probabilistic Approaches

Probability Review

Conditional Probabilities

Product Rule, Chain Rule, Bayes Rule

Bayes Nets & Naive Bayes

Learning, Independence, Conditional Indepenedence

Naive Bayes Derivation (discrete case plus smoothing)

Discrete inputs (Bernoulli, Multinomial)

Continuous Inputs

Logistic & Softmax Regression

Binomial Logistic Regression

Multinomial Logistic Regression

Linear Classifier, Hyperplane

Multinomial Logistic Regression Classifier (loss)

Softmax Classifiers